A self-learning neuro-fuzzy system
Often a major difficulty in the design of rule-based expert systems is the process of acquiring the requisite knowledge in the form of production rules. In most of expert systems, crisp or fuzzy if-then rules are generally derived from human experts using linguistic information. However, the initial linguistic rules are invariably rather crude and, although qualitatively correct, need to be refined to achieve better performance. In this paper, we present an innovative approach to the rule extraction directly from experimental numerical data for pattern recognition and system identification. This paper presents a neural network-based system which is trained in such a way that it provides an appealing solution to the problem of knowledge acquisition based on experimental numerical data. The values of the network's parameters, after sufficient training, are then utilized to generate both crisp and fuzzy if-then rules. The concept and method presented in this paper are illustrated through one traditional academic pattern recognition problem and one highly nonlinear system identification problem.
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